Modern businesses rely on computerized databases for operations. When business conditions change, such as during an acquisition or divestiture, database management becomes difficult and presents a challenge to both the acquiring entity, as well as the divested entity.
Prior art attempts to solve this problem have taken two primary forms: integration of data and migration of data. Data integration is performed through methods that include Enterprise Application Integration (“EAI”) (FIG. 1) and Service Oriented Architecture (“SOA”)(FIG. 2). EAI type solutions typically operate using a ‘hub and spoke’ architecture to transmit and receive database messages and data values at a client ‘hub’, and source the data from diverse applications at the spokes. SOA type solutions rely on intricate architectures and loosely coupled services to pass data among applications. In the SOA solutions, resources are made available as independent services. Those services are shared among diverse applications. SOA solutions also suffer from complex data structures, leading to increased latency in communications as well as requiring increased network capacity. Translation of data formats is included in integration methods, but these methods are limited to moving data back and forth. Disparate existing systems remain along with their data and business process inconsistencies and independent maintenance and operational costs.
Data migration includes methods of copying and moving data from a source to a target. Data migration allows for an extract of source data values, transformation of those values, and load into a target database (known as Extract, Transform, and Load—ETL). Data migration technologies typically involve creating programming code that moves data from point ‘a’ to point ‘b’ without regard to completeness, correctness, or consistency. The data conforms to the business rules of the target database.
It is therefore a challenge to develop a method to manage database operations that overcomes these, and other, disadvantages.